Adaptive Search-and-Training for Robust and Efficient Network Pruning

Xiaotong Lu1    Weisheng Dong1*    Xin Li2    Jinjian Wu1    Leida Li1    Guangming Shi1

1School of Artificial Intelligence, Xidian University      2 West Virginia University, Morgantown WV, USA

                Fig. 1: Detailed illustration of the adaptive search-and-training method. First, we sample compact subnets of the target network based on weight α. Second, we search, train, and map the subnets to the target network. Note that such a search-train-map process will be iterated until the sampled subnets converge. The best performing subnetwork is finally fine-tuned into the output pruned network.




Both network pruning and neural architecture search (NAS) can be interpreted as techniques to automate the design and optimization of artificial neural networks. In this paper, we challenge the conventional wisdom of training before pruning by proposing a joint search-and-training approach to learn a compact network directly from scratch. Using pruning as a search strategy, we advocate three new insights for network engineering: 1) to formulate adaptive search as a cold start strategy to find a compact subnetwork on the coarse scale; and 2) to automatically learn the threshold for network pruning; 3) to offer flexibility to choose between efficiency and robustness. More specifically, we propose an adaptive search algorithm in the cold start by exploiting the randomness and flexibility of filter pruning. The weights associated with the network filters will be updated by ThreshNet, a flexible coarse-to-fine pruning method inspired by reinforcement learning. In addition, we introduce a robust pruning strategy leveraging the technique of knowledge distillation through a teacher-student network. Extensive experiments on ResNet and VGGNet have shown that our proposed method can achieve a better balance in terms of efficiency and accuracy and notable advantages over current state-of-the-art pruning methods in several popular datasets, including CIFAR10, CIFAR100, and ImageNet.







[1] Lu X, Dong W, Li X, et al. Adaptive Search-and-Training for Robust and Efficient Network Pruning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.





                      title={Adaptive Search-and-Training for Robust and Efficient Network Pruning},
                     author={Lu, Xiaotong and Dong, Weisheng and Li, Xin and Wu, Jinjian and Li, Leida and Shi, Guangming},
                      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},











Table 1. Comparison of different pruning algorithms for ResNet on CIFAR10. ’FLOPS / pruned’ means the calculation and pruning rate. ’Acc / drop’ means accuracy and performance drop. The “3” and “7” under ”Pre-trained” and ”Fine-tuned” indicate whether the corresponding method needs to be pretrained before pruning or optimized afterward, respectively. Note that the data of SFP and FPGM are from the training log published by the authors, and the data of TAS are from the open source code of the author.




Table 2. Comparison of different pruning algorithms for ResNet on CIFAR-100. The parameters in the above table are consistent with
Table2, where “Ours (w/o ThreshNet)” means pruning with a fixed pruning rate for each layer in the network.


Table 3. Comparison of different pruning algorithms of ResNet50 & MobileNet V2 on ImageNet.





Xiaotong Lu, Email:

Weisheng Dong, Email:

Xin Li, Email:

Leida Li, Email:

Jinjian Wu, Email:

Guangming Shi, Email: